Many common signal processing operations may result in undesirable distortions. One such operation is the quantization of a signal. After an analog signal such as a video signal or an image is sampled, samples are typically quantized so that they can be represented as binary numbers of a finite bit size. Quantization is thus necessary to represent each sample within an allocated bit size. As a result of quantization, some information in the sample may be lost, resulting in noise referred to as quantization noise.
Quantization is not limited in digital to analog signal conversion. For example, digital signals are often re-quantized. For instance, digital samples may need to be re-quantized to reduce or increase the number of bits used to represent each sample. Such re-quantizing is often performed by video graphics adapters or printers that display or print digital images having a certain color depth, on a device capable of presenting images of a lower color depth.
To use a specific example, a graphics adapter may have a graphics processing engine that stores, and performs image processing operations on 10-bit pixels, but may use an 8-bit digital to analog converter (DAC) to generate analog signals for an interconnected display device. In this case, the 10-bit pixel values must first be quantized or re-quantized to the number of bits supported by the DAC (8-bits) prior to being displayed.
Similarly, color printers print a finite number of colors, represented by a nominal number of bits per pixel. Video images typically have more bits per pixel. Re-quantization from the video image's actual number of bits per pixel to the printer's nominal number of bits per pixel may form part of the color space conversion performed by the printer or printer software.
Quantization is a lossy procedure, in which the least significant bits of input samples are discarded. When smoothly shaded areas of an input image are quantized, color bands known as contours, are often observed in the quantized image due to the loss of visual information contained in the discarded bits. One technique for preserving the information that may be lost during quantization is to modulate the least significant bits of pixels in the input image with a dither signal, which is usually a random or pseudorandom signal or pattern, before quantization. Since the human visual system itself is a low-pass filtering process, the modulated information can be perceived when the quantized image is viewed.
Dither signals that are random or pseudorandom exhibit certain statistical properties that are desirable in signal processing. As the human visual system is more sensitive to noise in lower frequency bands, high-pass dither signals are desirable in image processing applications. In other words, high-pass dither signals are better than dither signals with a low-pass or a flat shaped spectrum because they contain only small amounts of energy at the lower frequency bands, while their energy at the higher frequency bands is effectively filtered out by the human visual system.
Although it is relatively straightforward to specify some desirable characteristics of dither signals using properties such as their probability density function (PDF), frequency spectrum and the like, the generation of suitable dither signals remains a challenge. In particular, dither signals suitable for image and video processing are more difficult to generate than those for audio processing.
Accordingly there remains a need for new dither signal generators suitable for use in image processing applications.